CrossRef Open Access 2019 1 sitasi

Gauging variational inference*

Sungsoo Ahn Michael Chertkov Jinwoo Shin

Lihat Sumber DOI

Abstrak

Abstract Computing of partition function is the most important statistical inference task arising in applications of graphical models (GM). Since it is computationally intractable, approximate methods have been used in practice, where mean-field (MF) and belief propagation (BP) are arguably the most popular and successful approaches of a variational type. In this paper, we propose two new variational schemes, coined Gauged-MF (G-MF) and Gauged-BP (G-BP), improving MF and BP, respectively. Both provide lower bounds for the partition function by utilizing the so-called gauge transformation which modifies factors of GM while keeping the partition function invariant. Moreover, we prove that both G-MF and G-BP are exact for GMs with a single loop of a special structure, even though the bare MF and BP perform badly in this case. Our extensive experiments indeed confirm that the proposed algorithms outperform and generalize MF and BP.

Penulis (3)

Sungsoo Ahn

Michael Chertkov

Jinwoo Shin

Format Sitasi

APA MLA BibTeX

Ahn, S., Chertkov, M., Shin, J. (2019). Gauging variational inference*. https://doi.org/10.1088/1742-5468/ab3217

Akses Cepat

Lihat di Sumber doi.org/10.1088/1742-5468/ab3217

Informasi Jurnal

Tahun Terbit: 2019
Bahasa: en
Total Sitasi: 1×
Sumber Database: CrossRef
DOI: 10.1088/1742-5468/ab3217
Akses: Open Access ✓